These data files contain raw, anonymized response data, as well as a data codebook, from the author survey conducted in May and June 2015 as a part of the Pay It Forward project. The survey was distributed to approximately 15,000 academics at the University of British Columbia, The Ohio State University, the University of California, Irvine, and the University of California, Davis, and received an overall response rate of 14.1%. Methods Survey conducted using Qualtrics software. Respondents included faculty, graduate students, and post-doctoral researchers from the University of British Columbia, The Ohio State University, the University of California, Irvine, and the University of California, Davis. The survey was open from May 20, 2015 to June 10, 2015. IRB approval for this study was obtained by the University of Tennessee, Knoxville, Office of Research Compliance.

We anonymously surveyed members and non-members of the LGBTQIA+ community of conservation students and professionals in North America to explore participants’ lived experiences in conservation regarding safety, belonging, and inclusion. Our 737 responses included 10% that identified as genderqueer, gender nonconforming, questioning, nonspecific, genderfluid, transgender woman, agender, transgender man, two spirit Indigenous, or intersex (hereafter gender expansive), and 29% bisexual, queer, lesbian, gay, asexual, pansexual, omnisexual, questioning, or non-heterosexual (hereafter queer+). Data also include results of a non-response survey of 157 individuals who chose not to complete our the full survey, but answered basic demographic questions to determine non-response bias., Responses were solicited from an email list that included natural resource, conservation, ecology, wildlife, and fisheries departments from public and private universities; 4-year colleges; 2-year colleges; professional schools; technical, vocational, or trade schools; Hispanic-serving institutions; historically Black colleges and universities; tribal colleges, and women’s colleges. To include perspectives from non-academic settings and to target LGBTIQA+ individuals, we included listserv members of the “Out in the Field'' LGBTQIA+ and ally working group of the Wildlife Society as part of our survey population. We distributed a Qualtrics suvey and consent letter to ask respondents about their feelings and experiences of safety, belonging, and inclusion working in the field of conservation., Data were analyzed in R version 4.2.2. , # LGBTQIA+ experiences in conservation survey data

https://doi.org/10.5061/dryad.rfj6q57gr

Survey data from 737 conservation students and professionals describing their lived experience and feelings on inclusion, safety, and belonging while working in the field of conservation. Data were used to describe lessened feelings of inclusion, safety, and belonging among LGBTQIA+ conservation professionals compared to non-LGBTQIA+ professionals. We also include a file of 157 individuals who did not respond to the main survey, but responded to a short survey of demographic questions to quantify non-response bias. Location data and extended text response data have been removed to protect survey respondents' anonymity.

Description of the data and file structure

Data are an anonymous output from a Qualtrics survey. Location information has been removed for further anonymity. Includes basic demographic information and quantitative ratings of feelings...

Despite many innovative ideas generated in response to COVID-19, few studies have examined community preferences for these ideas. Our study aimed to determine university community members’ preferences for three novel ideas identified through a crowdsourcing open call at the University of North Carolina (UNC) for making campus safer in the pandemic, as compared to existing (i.e. pre-COVID-19) resources. An online survey was conducted from March 30, 2021 – May 6, 2021. Survey participants included UNC students, staff, faculty, and others. The online survey was distributed using UNC’s mass email listserv and research directory, departmental listservs, and student text groups. Collected data included participant demographics, COVID-19 prevention behaviors, preferences for finalist ideas vs. existing resources in three domains (graduate student supports, campus tours, and online learning), and interest in volunteering with finalist teams. In total 437 survey responses were received from 228 (52%) staff, 119 (27%) students, 78 (18%) faculty, and 12 (3%) others. Most participants were older than age 30 years (309; 71%), women (332, 78%), and white (363, 83.1%). Five participants (1%) were gender minorities, 66 (15%) identified as racial/ethnic minorities, and 46 (10%) had a disability. Most participants preferred the finalist idea for a virtual campus tour of UNC’s lesser-known history compared to the existing campus tour (52.2% vs. 16.0%). For graduate student supports, 41.4% of participants indicated no preference between the finalist idea and existing supports; for online learning resources, the existing resource was preferred compared to the finalist idea (41.6% vs. 30.4%). Most participants agreed that finalists’ ideas would have a positive impact on campus safety during COVID-19 (81.2%, 79.6%, and 79.2% for finalist ideas 1, 2 and 3 respectively). 61 (14.1%) participants indicated interest in volunteering with finalist teams. Together these findings contribute to the development and implementation of community-engaged crowdsourced campus safety interventions during COVID-19. Methods An online survey was distributed to members of the UNC Chapel Hill community using multiple digital strategies, including a mass informational email system (UNC’s Mass Mail system), circulation on 12 departmental listservs, UNC GroupMe text messages, and the Research For Me @ UNC database. Survey responses were collected via a Qualtrics survey form. Survey responses were collected online from March 30, 2021 to May 6, 2021. Survey participants completed electronic informed consent prior to answering the survey. All survey response data collected from participants were compiled using Microsoft Excel. Data collected include demographic information of participants, questions about COVID-19-related behaviors, and preferences for crowdsourced strategies for enhancing campus safety during the pandemic vs. existing comparable resources at UNC.

The data relates to the paper that analyses the determinants or factors that best explain student research skills and success in the honours research report module during the COVID-19 pandemic in 2021. The data used have been gathered through an online survey created on the Qualtrics software package. The research questions were developed from demographic factors and subject knowledge including assignments to supervisor influence and other factors in terms of experience or belonging that played a role (see anonymous link at https://unisa.qualtrics.com/jfe/form/SV_86OZZOdyA5sBurY. An SMS was sent to all students of the 2021 module group to make them aware of the survey. They were under no obligation to complete it and all information was regarded as anonymous. We received 39 responses. The raw data from the survey was processed through the SPSS statistical, software package. The data file contains the demographics, frequencies, descriptives, and open questions processed.     The study...

Fertilizers and deicers are common materials for property maintenance in the Midwest, however, their application contributes to negative environmental impacts when applied incorrectly. While fertilizer use is well researched, deicer use on private properties is not. This research aims to ascertain whether patterns of fertilizer use are different from those of deicer use in Hamilton County, Ohio, and determine what factors influence a resident’s decision to use these materials. Survey data were collected from 110 single-family households (38.9% response rate). Respondents are motivated by property appearance to apply fertilizers. Deicer use stems from safety concerns. Respondents were significantly more likely to consider the environmental impact of fertilizers than deicers. Respondents felt that using deicers is a more neighborly practice while using fertilizers reflects more positively on them in their neighborhood. This information can be used to develop outreach programs to reduce the environmental impacts of fertilizers and deicers. Methods A survey was designed to gauge respondents' perceptions and usage of fertilizers and deicers. Questions included in the survey asked respondents about the frequency with which residents use fertilizer and deicer, perceptions and knowledge of these materials, and demographic information (e.g., age, income, education, gender). Previous studies which focused on individuals’ uses of fertilizers, deicers, and other lawn management practices were used as a guide for designing questions for this survey. A random sample of 300 single family homes in Hamilton County was selected to receive the survey materials using ArcGIS Pro 2.9.2 and parcel data downloaded from the Hamilton County Community Planning Maps and GIS website in May of 2022. The surveys, as well as a $2 bill incentive, were distributed and collected using the Drop-off Pick-up (DOPU) method. Each survey packet contained a cover letter and printed cover sheet entitled “Research Consent Form” which informed potential participants about their rights as a survey participant. The cover sheet specified that answering the questions on the survey was completely voluntary and that the data participants provided would be anonymized and presented in aggregate form so that no one individual or household could be identified. No participant under 18 years of age was recruited and the cover letter stated that “Participation in this research is restricted to persons 18 years of age or older”. Lastly, the consent form provided contact information for the researchers and our Research Ethics and Integrity Office. Placing the fully or partially completed survey for the researchers to retrieve was understood as providing informed consent. The survey instrument, consent form, and recruitment mechanism were approved by the Research Ethics and Integrity Office at Miami University (project # 04247e). The dates of recruitment of participants, distribution and collection of survey materials took place from the June 1st to August 20th, 2022. Completed survey responses were recorded using Qualtrics. ArcGIS Pro was used to classify land cover and area for each household selected for surveying. The land covers on each parcel were digitized and divided into the following categories: lawn, building, driveway, sidewalk, patio, and pool. The various land cover classifications and their surface area for each parcel were used to calculate suggested fertilizers and deicer amounts for each household. These suggested amounts were compared to the amounts self-reported by respondents in the surveys.

The Cardiff Travel Survey is a longitudinal survey that aims to (a) establish current and previous (before the coronavirus outbreak) travel habits; (b) explore how travel-related attitudes, social norms and perceptions change over time; and (c) examine the interplay between individual (perceptual) and environmental (infrastructural) factors in travel mode choice, in particular in relation to the uptake of active travel such as walking and cycling in the City of Cardiff, Wales. The Cardiff Travel Survey 2024 (Wave 4) is an opportunity sample that was collected in 2024 (n=2,427) by the Centre for Climate Change and Social Transformations (CAST), and is the fourth of a longitudinal series of surveys to be held annually for the duration of the centre. Data for the Cardiff Travel Survey 2024 were collected between 11 April 2023 and 05 June 2024. Participants of the Cardiff Travel Survey 2023 who consented (n=1,324) were recontacted via email to invite them to take part in the 2024 survey. Furthermore, participants were recruited through posts on social media, such as Facebook® and Twitter®. Invitations were posted on CAST and investigator accounts. The survey was hosted on the Qualtrics online survey platform and available in both English and Welsh. Inclusion criteria were that participants had to be at least 18 years of age and live in or travel regularly to Cardiff. The English version of the survey was completed by 2,628 respondents and the Welsh version by 9 respondents. There was evidence of bot activity in the English survey. This led to 1,630 responses to be removed. Incomplete responses (n=129), defined as those without any answers beyond socio-demographic, were removed from the dataset. A further 81 respondents did not complete the first section on current travel behaviours and were also removed. This left a final sample of n=797 adults. Participants were asked to create a unique code that can be used match this survey to the previous and next surveys without knowing their identity. Main topic areas of the questionnaire were: Demographics, Travel behaviours, Physical activity, Physical health and mental wellbeing, Perceptions of infrastructure and environmental quality, Travel-related social identity, Attitudes to active travel, Active travel related social norms, Support for active travel policies, and Unique ID.The Centre for Climate Change Transformations (C3T) will be a global hub for understanding the profound changes required to address climate change. At its core, is a fundamental question of enormous social significance: how can we as a society live differently - and better - in ways that meet the urgent need for rapid and far-reaching emission reductions? While there is now strong international momentum on action to tackle climate change, it is clear that critical targets (such as keeping global temperature rise to well within 2 degrees Celsius relative to pre-industrial levels) will be missed without fundamental transformations across all parts of society. C3T's aim is to advance society's understanding of how to transform lifestyles, organisations and social structures in order to achieve a low-carbon future, which is genuinely sustainable over the long-term. Our Centre will focus on people as agents of transformation in four challenging areas of everyday life that impact directly on climate change but have proven stubbornly resistant to change: consumption of goods and physical products, food and diet, travel, and heating/cooling. We will work across multiple scales (individual, community, organisational, national and global) to identify and experiment with various routes to achieving lasting change in these challenging areas. In particular, we will test how far focussing on 'co-benefits' will accelerate the pace of change. Co-benefits are outcomes of value to individuals and society, over and above the benefits from reducing greenhouse gas emissions. These may include improved health and wellbeing, reduced waste, better air quality, greater social equality, security, and affordability, as well as increased ability to adapt and respond to future climate change. For example, low-carbon travel choices (such as cycling and car sharing) may bring health, social and financial benefits that are important for motivating behaviour and policy change. Likewise, aligning environmental and social with economic objectives is vital for behaviour and organisational change within businesses. Our Research Themes recognise that transformative change requires: inspiring yet workable visions of the future (Theme 1); learning lessons from past and current societal shifts (Theme 2); experimenting with different models of social change (Theme 3); together with deep and sustained engagement with communities, business and governments, and a research culture that reflects our aims and promotes action (Theme 4). Our Centre integrates academic knowledge from disciplines across the social and physical sciences with practical insights to generate widespread impact. Our team includes world-leading researchers with expertise in climate change behaviour, choices and governance. We will use a range of theories and research methods to fill key gaps in our understanding of transformation at different spatial and social scales, and show how to target interventions to impactful actions, groups and moments in time. Participants for the Cardiff Travel Survey (Wave 4) were recruited through posts on social media, such as Facebook and Twitter. Invitations were posted on CAST, Cardiff University, and investigator accounts. The survey was hosted on the Qualtrics online survey platform and available in both English and Welsh.

A comparative evaluation of ChatGPT 3.5 and ChatGPT 4 in responses to selected genetics questions - Full study data

https://doi.org/10.5061/dryad.s4mw6m9cv

This data was captured when evaluating the ability of ChatGPT to address questions patients may ask it about three genetic conditions (BRCA1, HFE, and MLH1). This data is associated with the JAMIA article of the similar name with the DOI 10.1093/jamia/ocae128

Description of the data and file structure

1. Key: This tab contains the data structure, explaining the survey questions, and potential responses available.
2. Prompt Responses: This tab contains the prompts used for ChatGPT, and the response provided from each model (3.5 and 4)
3. GPT 4 Results: This tab provides the responses collected from the medical experts (genetic counselors and clinical geneticist) from the Qualtrics survey.
4. Accuracy (Qx_1): This tab contains the subset of results from both the Ch...

Trophy hunting is one of the most contentious issues in recent biodiversity conservation discourse, eliciting opposition and support for the practice. Ethical concerns are often at the heart of the debate. To investigate moral views about trophy hunting, we conducted an online survey of randomly selected scholars worldwide who had published on biodiversity conservation (n = 2,315). Scholars expressed divergent views on the moral acceptability of trophy hunting as a conservation practice. Moral convictions were significantly related to the perspectives The most important factor in predicting the moral views of the respondents was the consequences of trophy hunting for local human communities. The results also indicated that utilitarian (versus deontological) decision-making in conservation, ecological consequences of trophy hunting, and animal welfare issues contribute to the divergent views. The findings emphasize the need for interdisciplinary work on ethical issues concerning animal rights and welfare in conservation, as well as providing robust and comprehensive evidence on the consequences of trophy hunting for local communities. We caution that polarization among conservation scholars may negatively affect conservation efforts. Based on the literature and our findings, we provide some recommendations to narrow the gap and consider different management options.

Methods In November and December 2020, we conducted a web-based survey of biodiversity conservation scholars who had published in the scientific literature since 2010. We used the publications listed in the 'Web of Science – All Databases' as the sampling frame and searched for publications using the search term: 'biodiversity conservation' OR 'wildlife conservation' OR 'conservation biology' OR 'trophy hunting' in the 'topic' field. We obtained the authors' email addresses from the same database and sent individualized email invitations with a link to the Web survey hosted by the Qualtrics survey platform. Qualtrics only accepted one response per link, avoiding the possibility of a respondent sharing their link with unidentified respondents. Two additional follow-up invitations were sent within two weeks of the initial invitation to those who did not respond to the earlier invitation. The instructions in the invitation email and survey noted that it was limited to the authors who had published work in the area of biodiversity conservation. Additionally, at the beginning of the questionnaire, we asked the respondents if their work, study, or research was related to biodiversity conservation. Those who responded 'no' to this question were automatically excluded from the survey. We also asked the respondents to provide their opinions on trophy hunting in the context of the developing world. For clarity and consistency in the responses, the following definition of trophy hunting was provided to the respondents on multiple pages throughout the survey: “Trophy hunting is a type of selective recreational hunting of animals done to obtain their body parts as a representation of success or memorial,” with an emphasis on 'developing countries' (see Appendix S1). The Institutional Review Board of Texas A&M University approved the data collection protocols and the survey instrument (IRB2020-1228M). Of the 26,064 scholars who received the invitation, 3,794 responded (response rate: 14.5%), and 2,430 completed the questionnaire (completion rate: 64.0%). We used the authors’ contact information at the time of publication. Many had likely changed their institutional affiliation since publication (beginning in 2010). We cannot discern how many authors had changed their affiliations and, consequently, did not receive the invitation. Furthermore, we only sent invitations to authors whose email addresses were available through the database (all co-authors). After screening out responses from scholars whose work or research did not involve biodiversity conservation (n = 106) and those who did not answer our outcome variable (n = 9), we included 2,315 cases in the analyses.

Survey objectives:

The Enviro Pulse Survey provides regular, high level environmental social indicators to several divisions within NSW Department of Climate Change, Energy, the Environment and Water (DCCEEW) in support of the NSW Climate Change Fund Policy Framework and the NSW Climate Change Adaptation Strategy.

The Enviro Pulse Survey questionnaire was developed in 2020, in collaboration with policy and program teams at the Department, and with support from James Cook University. This work was underpinned by a modified Drivers-Pressures-State-Impacts-Responses (DPSIR) framework. The survey has been enhanced over time by the DCCEEW Social Science team to support new strategic needs and address knowledge gaps. The survey data is used to inform and support environmental policy, research, programs and evaluations at DCCEEW. It helps to reduce the need for expensive ad hoc research consulting and effort duplication.

The survey is administered to a representative sample of NSW population four times a year, to understand trends, and measure variance across space and between social groups.

It aims to provide ongoing information to DCCEEW on:

• NSW residents' access to environmental assets and resources;
• Environmental vulnerability and resilience;
• Community values, and support for policy decisions;
• Environmental motivations and behaviours.

Survey methodology:

The Enviro Pulse survey is administered quarterly, and is issued as a 20 minute online questionnaire to a total of n=1,000 residents of NSW aged 18 and over. The first survey was conducted in December 2020.

The questionnaire is modular in structure: - Key indicators are measured every three months. - Remainder of the survey topics (e.g. connection to place, concern for environment, energy use and motivations) are alternating every other survey wave, i.e. data for each such thematic module is collected every six months.

The study tracks trends over time and most of the questions have remained the same throughout the survey program. Minor amendments have occasionally been made to the questionnaire, and new questions have been added to support emerging strategic needs.

The survey was built and is hosted using the Qualtrics survey platform. A number of data quality checks are conducted at launch of each survey pulse, and on delivery of the final data of each wave.

Qualtrics is responsible for sourcing participants from several market research panel providers. Quotas have been set by key demographics to ensure a representative sample. The final results are weighted by age group, gender, regional proportions, and Aboriginal status for NSW population. It is acknowledged that some groups may be underrepresented in the final sample - such as residents with limited English skills, residents with low or no formal education, etc.

Data accuracy, reporting:

Results are reported on an aggregated level in order to protect the privacy and anonymity of individual respondents, to meet social research industry standards, and to ensure the robustness of the results.

At the aggregate NSW level, the survey has high levels of accuracy, due to the large sample size (i.e. approximately 1,000 responses per wave). Typically, at the 95% confidence level, the margin of error on the reported survey results is approximately +/- 3% points or less. This means that the difference between two reported results may have to be at least 6% points for the gap to be considered statistically significant. Statistically significant changes over time have been highlighted in the attached survey results, where applicable.

External events - such as Covid-19 pandemic related public health orders, extreme weather events in NSW - so far have not impacted the ability to gather sample for the study. However, as this is a social research dataset, it is expected that such external events have an impact on the environmental attitudes and behaviours that the survey has been designed to collect information on, and may explain some of the variance in the results over time.

...

Feedback and questions: SocialResearch@environment.nsw.gov.au

Survey objectives:

The community appreciation of biodiversity (CAB) indicator is one of the measures in the NSW Government's Biodiversity Indicator Program reporting. The indicator is based on a set of survey questions to assess and track changes in community understanding and support of biodiversity conservation across 3 key dimensions:

• cognitive appreciation – whether people are aware of biodiversity and its benefits or values
• affective appreciation – how much people value biodiversity and whether they care about it
• behavioural appreciation – whether people are engaged in actions that protect or benefit biodiversity.

More information about the Biodiversity Indicator Program and the latest 2024 Biodiversity Outlook Report is available here: https://www.environment.nsw.gov.au/topics/animals-and-plants/biodiversity/biodiversity-indicator-program

Enhanced survey instrument:

The CAB indicator was conceptualised and developed by an external group of researchers from University of Queensland, Queensland University of Technology and CSIRO. The first assessment of the indicator repurposed data from the 2015 ‘Who cares about the environment?’ survey to help understand community appreciation of biodiversity across the 3 dimensions. The findings were published in 2021.

The same external team of researchers developed an enhanced CAB indicator method for future use. The second assessment in 2022 adopted the same 3 dimensions as the first assessment, but using a purpose-built survey tool. The enhanced survey instrument retained the 22 'Who cares' survey questions used in the first assessment, for comparison and continuity, and incorporated 52 additional questions which allow the indicator to be more comprehensively assessed.
The attached 'Developing enhanced measures' report describes the development and features of this enhanced indicator. The report is also available here: https://www.environment.nsw.gov.au/research-and-publications/publications-search/community-appreciation-of-biodiversity-indicator-developing-enhanced-measures

Since 2022, the NSW DCCEEW Social Science team have collected data using the enhanced CAB indicator survey on an annual basis, to track trends over time.

Methodology and reporting:

The enhanced CAB survey is issued as a 12-minute online questionnaire to a total of approximately 2,000 residents of NSW aged 18 and over. The survey was built and is hosted using the Qualtrics survey platform. A number of data quality checks are conducted at launch of each survey, and on delivery of the final data.

Qualtrics is responsible for sourcing participants from several market research panel providers. Quotas have been set by key demographics to ensure a representative sample. The final results are weighted by age group, gender, regional proportions, and Aboriginal status for NSW population. It is acknowledged that some groups may be underrepresented in the final sample - such as residents with limited English skills, residents with low or no formal education, those with limited access to internet etc.

External events - such as Covid-19 pandemic related public health orders, extreme weather events in NSW - so far have not impacted the ability to gather sample for the study. However, as this is a social research dataset, it is expected that such external events may have an impact on the environmental attitudes and behaviours that the survey has been designed to collect information on, and may explain some of the variance in the results over time.

Results are reported on an aggregated level in order to protect the privacy and anonymity of individual respondents, to meet social research industry standards, and to ensure the robustness of the results.

At the aggregate NSW level, the survey has high levels of accuracy, due to the large sample size of approximately n=2,000 responses per wave. Typically, at the 95% confidence level, the margin of error (MoE) on survey results reported on population level is approximately +/- 2.2% points or less.

Please contact Social Science Team at SocialResearch@environment.nsw.gov.au with any questions or feedback.

Quality of life among patients with atrial fibrillation https://doi.org/10.5061/dryad.gtht76hsf | Element | Notes | | ------- | ----- | | Title * | Quality of life among patients with atrial fibrillation: A theoretically-guided cross-sectional study | | Creator * | Dr. Kathy L Rush | | Description * | The primary motivation behind the creation of this dataset is to bring attention to the notably reduced health-related quality of life (HRQoL) individuals suffering with atrial fibrillation (AF) experience when compared to both the general population and individuals with other heart-related conditions. Current research tends to concentrate on understanding how AF symptoms impact HRQoL, often overlooking the significance of individual characteristics determining HRQoL. To bridge this research gap, this study aims to establish an enhanced predictive model for HRQoL in individuals with AF. This model is based on an adapted HRQoL conceptual framework that takes into account both the influence of symptoms and the unique characteristics of each individual. | | Alternate Title | Quality of life among patients with atrial fibrillation | | Contact Name | Dr. Kathy L Rush | | Contact Email | kathy.rush@ubc.ca | | Contact Other | | | Update Frequency * | One time upload September 2023 | | Date Issued | September 2023 | | Date Created * | Data collection began November 2020 | | Start Date | 11/1/2020 | | End Date | 10/31/2021 | | Spatial Coverage | British Columbia | | Usage Considerations | This dataset is used to answer the associated research questions and fulfill the purpose of the study. We examined whether individual characteristics (overall mental health, perceived stress, sex, age, AF knowledge, household and recreational physical activity) incremented prediction of HRQoL and AF treatment satisfaction beyond AF symptom recency and overall health | ## Methodology Sample and Recruitment All patients of the clinic with an AF diagnosis who were over 18 years and could complete an online survey or had a family member who could assist, were eligible to participate. The clinic’s booking clerk sent a letter detailing the research study (by mail or email) to all patients with upcoming appointments during the recruitment period. The letter informed patients of the ongoing study and to expect a telephone initiation from a research team member regarding their eligibility and interest in the study. Patient contact information was then shared with the research team using secure file transfer. Subsequently a research assistant (a physician or a licensed practical nurse) who had no prior relationship with participants contacted patients by telephone. Recruitment began in November 2020 and continued for one year until a sample size of approximately 200 was achieved. A post hoc power analysis assuming a medium effect size estimated required sample size for modelling to be 114, indicating appropriate sample size had been achieved for analyses (Faul et al., 2007). Data Collection Study data were collected using an online survey hosted on Qualtrics (Qualtrics, Provo, UT). Prior to taking the survey, all participants gave electronic consent. Participants who finished the survey were eligible for a chance to win one of three $150 gift certificates through a random draw. Measures Overall Health: Participants were asked to rate their overall health on a scale ranging from 1 (poor) to 4 (excellent) (Ware et al., 1996). Overall mental health: Participants were asked to rate their overall mental health on a scale ranging from 1 (poor) to 4 (excellent) (Ahmad et al., 2014). Perceived stress (S. Cohen et al., 1983): The Perceived Stress Scale (PSS-10), a 10-item, 5-point scale, measures the degree to which situations in one's life are appraised as stressful, ability to control aspects of life, confidence in handling problems, or being unable to cope with demands. The PSS-10 previously had a reliability alpha of .78 and correlated in a predictable way with other measures of stress (S. Cohen et al., 1983) Socio-demographic characteristics: These included sex, age, marital status, race/ethnicity, education, and income. AF Knowledge (McCabe et al., 2020). The Knowledge about AF tool is a 28-item multiple choice-style questionnaire including questions about AF symptoms, treatment, medications, risk factors, and lifestyle. Participants are asked to choose one of 3 options for each question, only one of which is the correct response. The tool was developed using research on gaps in patient knowledge and patient values and management preferences. Knowledge scores are calculated as a percentage of correct answers, with higher numbers indicating higher knowledge. Four items were removed from the overall knowledge percent scores, as per McCabe et al. (McCabe et al., 2020) finding that these items had factor loadings below .45 and were not reliable predicto...

These extended supplementary materials go with the article:

Witzel & Dewis (2022) Why Bananas Look Yellow: The Dominant Hue of Object Colours. Vision Research.

A. SURVEYS

A pdf-printout for each of the three Qualtrics surveys illustrates details of the procedure. The layout may have been slightly different in Qualtrics (e.g., wide screen vs portrait display). Also note that the second and third surveys feature a few questions that were unrelated to the dominant-hue study (identifying a grey image).

B. STIMULI

The images used in Experiments 1-3, and the animated images used as cues to colour changes in Experiment 3 are packed in zip-files.

C. CODE

The Matlab code "onehue_maker.m" is a function that implements the dominant-hue algorithm to produce one-hue images like those in the experiments. To try out the program, the photo of the banana and the mask identifying its background are also uploaded (= first and second input to the function). The purpose of the mask is to remove the background colour from the dominant-hue computations.

D. DATA

The uploaded data is not completely raw but has been polished in the following ways:

Pilot data has been removed (i.e., meaningless data from us and our students to try out, check and polish the survey).

Incomplete runs have been removed (i.e., when participants quitted before completing the whole survey).

Data irrelevant to this study have been removed (date and time; grey-identification task [see above]).

There are 3 sheets with data and three sheets with stimulus specifications for each of the three experiments. The stimulus specifications include the measures used in the analyses in "Other Factors" in the Discussion of Experiment 3.

Columns in the Data sheets are:

Participant information: recruit (soc med = social media; UG pool = undergraduate students, prolific = https://www.prolific.co/); coldef = Colour deficiencies (1 Yes, 2 No according to test, 3 No without test, 4 Don't know); sex (1 male, 2 female, 3 other); age (in years), and duration (in minutes).

Main data: Column labels are composed of the following elements, separated by an underscore (_):

The first 3-5 letters of the object name: ban = banana, car = carrot, cher = cherry, dress = #theDress, fro = frog, gra = grapes, lem = lemon, let = lettuce, ora = orange, pig, ros = rose, shoe = #theShoe, stra = strawberry, zuc = zucchini/courgette.

A symbol indicating the stimulus condition: 1 = One-Hue, m = Minus-Hue Rotation, p = Plus-Hue Rotation.

A number identifying the measure: 1 = responded position; 2 = accuracy of the response (1 = correct); 3 = response time (in sec), 4 (Experiment 2-3) = confidence rating (between 0 and 100), 5 (Experiment 3) = cue confidence (cf. Figure 11.a).

For inverted colours (Experiment 3), the column label starts with an "i" (for inverted).

Practice Trials: Start with the prefix ex (for example) followed by an underscore (_) and the ID of the object; otherwise, data as in main trials.

Catch Trials (Experiment 2-3): Start with object name "d" for disk, otherwise, data as in main trials.

Eidolon Guesses (Experiment 2): Start with "guess" followed by the object ID (see main trials) followed by a number indicating the measure: 1 = response (yes/no), 2 = confidence (if positive response). In case of a positive response, the text entries are save in the variables starting with guess_txt.

Columns in the stimulus sheets are:

DomHue: Angle of the dominant hue (cf. Figure 3); as principal components are relative to the average, the angle is relative to the average, not the origin.

pole1 and pole2: Poles of the dominant hue direction. "pole1_rgb" provides corresponding RGBs for illustration (cf. Figure 1).

ChromaRescaled: Rescale Factor (see Experiment 3).

MaxChr: Maximum chroma of the colour distribution in CIELUV.

M: Average chromaticities (u*, v*) of the colour distribution.

pc: Coefficients of the first principal component for u* and v*.

latent & expl: Absolute and relative explained variance, respectively; second column corresponds to orthogonal variance.

hueM & hueSD: Average and standard deviation of the hue of the colour distribution (cf. Figure 3).

rot_minus, rot_plus: The hue rotations in the rotated-hue condition (constant minus or plus 5, except for #theShoe).

oog_1hue, oog_plus, oog_minus: The proportion of out-of-gamut values.

oogdist_1hue, oogdist_minus, oogdist_plut: Average difference between clipped and original images (in CIELUV).

Mshift_1hue, Mshift_minus, Mshift_plus: Average and standard deviation of chromaticity shift due to the experimental manipulation (cf. Figure 5 and Table S1).

Mhueshift_1hue, Mhueshift_minus, Mhueshift_plus: Average and standard deviation of hue shift in CIELUV (cf. Figure S4.d-f and Table S2).

Lab_shift_1hue, Lab_shift_minus, Lab_shift_plus: Average and standard deviation of chromaticity shift in CIELAB (cf. Figure S4.a-c and Table S1).

Lab_hueshift_1hue, Lab_hueshift_minus, Lab_hueshift_plus: Average and standard deviation of hue shift in CIELAB (cf. Figure S4.g-i and Table S2).

Lab_Mhue: Hue of the average colour in CIELAB

Lab_hueM & Lab_hueSD0: Average and standard deviation of the CIELAB hue distribution.

huehist0: CIELUV hue histogram; each entry corresponds to the frequencies for 72 bins of 5-deg (cf. Figure 3); the zero indicates that the hue is relative to the origin, not to the average chromaticity.

In response to the COVID-19 pandemic, Edinburgh Napier University’s Transport Research Institute has been undertaking a study, funded by the Scottish Funding Council (SFC), into its impact on transport and travel in Scotland. As part of this research, a travel behaviour questionnaire was developed focusing on daily travel as well as people’s long-term travel habits, attitudes and preferences during the different phases of the pandemic outbreak. The associated questionnaires were completed by participants between 3rd February 2021 and 17th February 2021 using the online platform, Qualtrics. The survey was restricted to Scottish residents and involved enforcing quota constraints for age, gender and household income. A total of 994 responses were collected. Perceptions of risk, trust in information sources and compliance with COVID-19 regulations were determined together with changes in levels of ‘life satisfaction’ and modal choice following the onset of COVID-19. In addition, survey responses were used to identify anticipated travel mode use in the future. Consideration was also given to the effects of COVID-19 on transport related lifestyle issues such as ‘working from home’, online shopping and the expectations of moving residences in the future. The research provided an insight into both the relationships between the levels of non-compliance with COVID-19 regulations and demographic variables and the respondent attributes which might affect future public transport usage. In general, the study confirmed significant reductions in traffic activity, amongst respondents during the COVID 19 pandemic associated with walking, driving a car and either using a bus or train. The respondents also indicated that they anticipated they would continue to make less use of buses and trains at the end of the pandemic.

In response to the COVID-19 pandemic, Edinburgh Napier University’s Transport Research Institute has been undertaking a study, funded by the Scottish Funding Council (SFC), into its impact on transport and travel in Scotland. As part of this research, a travel behaviour questionnaire was developed focusing on daily travel as well as people’s long-term travel habits, attitudes and preferences during the different phases of the pandemic outbreak. The associated questionnaires were completed by participants between 3rd February 2021 and 17th February 2021 using the online platform, Qualtrics. The survey was restricted to Scottish residents and involved enforcing quota constraints for age, gender and household income. A total of 994 responses were collected. Perceptions of risk, trust in information sources and compliance with COVID-19 regulations were determined together with changes in levels of ‘life satisfaction’ and modal choice following the onset of COVID-19. In addition, survey responses were used to identify anticipated travel mode use in the future. Consideration was also given to the effects of COVID-19 on transport related lifestyle issues such as ‘working from home’, online shopping and the expectations of moving residences in the future. The research providedan insight into both the relationships between the levels of non-compliance with COVID-19 regulations and demographic variables and the respondent attributes which might affect future public transport usage. In general, the study confirmed significant reductions in traffic activity, amongst respondents during the COVID 19 pandemic associated with walking, driving a car and either using a bus or train. The respondents also indicated that they anticipated they would continue to make less use of buses and trains at the end of the pandemic.

Web-based information systems designed to increase access to species occurrence data for use in research and natural resource decision-making have become more prevalent over the past few decades. The effectiveness of these systems depends on their usability and extent of use by their intended audiences. We conducted an online survey of academics and government professionals in the United States to compare their species occurrence data needs and their perceptions and use of web-based species occurrence information systems. Our results indicate that although views and perceptions held by academics and government professionals about the importance, usefulness, and ease of use of these information systems tend to be similar, there were differences in their use of species occurrence data and web-based species occurrence information systems. The baseline information obtained in this study will help inform future directions for improvements in species occurrence information systems.

Methods A survey consisting of a combination of close-ended questions and open-ended questions was developed to examine use of species occurrence data and web-based species occurrence information systems by professionals in the United States. This dataset only includes responses to closed-ended questions of the survey. Most closed-ended questions were developed as individual Likert-type items. Selection of one response was allowed per closed-ended question except for those that included ‘select all that apply’.

A list of names and emails of potential survey participants was compiled from websites of relevant academic departments in universities of the United States classified as doctoral universities of highest research activity in the 2015 Carnegie Classification of Institutions of Higher Education. Names and emails of potential government participants were compiled from websites of natural resource agencies in each of the fifty states of the United States and the federal government.

The survey was administered online via Qualtrics. Invitations to participate in the online survey were sent from Qualtrics to academic and government professionals. A generic link to the survey was also provided to participants who had expressed interest in inviting other colleagues to participate in the survey. The online survey was open to all participants for one month in March 2017. The survey was opened a second time in June 2017 to invitees from a government agency that required clearance before staff could participate in the survey. The online survey was permanently closed on July 2017.

OVERVIEW

Title of Dataset: Urinary incontinence in female weightlifters

Reference: doi: 10.1371/journal.pone.0278376. PMID: 36449558; PMCID: PMC9710785.

Author Information

Name: Marianne Huebner Institution: Michigan State University Address: East Lansing, MI 48824

Period of data collection: 27 April – 20 May 2022

Geographic region of data collection: Online survey in USA with participants from 29 countries in IWF regions Africa, Asia, Europe, Oceania, PanAmerican

LIST OF FILES Dataset: wlisi_zenodo.xlsx Data dictionary: wlisi_meta.xlsx

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data: The survey was distributed by the Master Committee of the International Weightlifting Federation (IWF) to the National Master Chairs. They then used email or social media to communicate the study to the women weightlifters. The Survey was available in four languages (English, German, French, Spanish), translated and tested by native speakers. In addition, the survey was advertised in weightlifting interest groups via Facebook and Instagram. The survey was administered online via Qualtrics (Provo, UT, USA).

Methods for processing the data: Data were downloaded from Qualtrics (Provo, UT, USA) to Excel and then pre-processed in the statistical software R v. 4.0.3. (https://www.r-project.org)

Variable formats (numeric, character) were checked and transformed, as appropriate.

Data quality checks: Exclusion criteria were younger than 30 years (n=1), missing age (n=1), currently pregnant (n=3). To account for the possibility of male participants missing responses to age of menstruation or prior pregnancies (n=15), were also excluded. Since the focus was on competitive weightlifters, missing response to age of first competition (n=34) or no snatch or clean and jerk in the last 6 months (n=14) were also exclusion criteria. This resulted in an analysis data set of 824 women. Univariate distributions were evaluated numerically and graphically.

DATA-SPECIFIC INFORMATION

Number of variables: 27

Number of cases/rows: 824

Variable List: wlisi_meta.xlsx

Missing data codes: NA